A Deep Learning Cyber Security Algorithm For P2p File Sharing In Internet Community

Abstract

Botnet is able to make use of a wide range of malicious software, including worms, rootkits, Trojan horses, etc., it has become the most widespread security concern in recent years. They have been put to use in the transmission of links that lead to phishing schemes, the launching of attacks, and the providing of services that are dangerous over the internet. The final stage of categorization has been completed, and the study is now concentrating on the most important stage, which is developing relevant metrics. These metrics will measure and differentiate bot-based operations from human-based operations. Previously, the study had been concentrating its efforts on the very last stage of the classification process. In this paper, we are developing a model based on deep learning with the objective of lowering the level of risk that is connected with P2P file sharing. The deep learning approach was developed as a tool for cyber security since it provides accurate and efficient instance classification. This was accomplished through the method. This study examines data obtained from the network concerning the attacks that are now garnering the most attention in order to uncover successful outcomes. When compared to the results obtained by utilizing a variety of other classifiers, our findings suggest that the proposed framework is relevant.